ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks

The radio map, serving as a visual representation of electromagnetic spatial characteristics, plays a pivotal role in assessment of wireless communication networks and radio monitoring coverage. Addressing the issue of low accuracy existing in the current radio map construction, this paper presents a novel radio map construction method based on generative adversarial network (GAN) in which the Aggregated Contextual-Transformation (AOT) block, Convolutional Block Attention Module (CBAM), and Transposed Convolution (T-Conv) block are applied to the generator, and we name it as ACT-GAN. It significantly improves the reconstruction accuracy and local texture of the radio maps. The performance of ACT-GAN across three different scenarios is demonstrated. Experiment results reveal that in the scenario without sparse discrete observations, the proposed method reduces the root mean square error (RMSE) by 14.6% in comparison to the state-of-the-art models. In the scenario with sparse discrete observations, the RMSE is diminished by 13.2%. Furthermore, the predictive results of the proposed model show a more lucid representation of electromagnetic spatial field distribution. To verify the universality of this model in radio map construction tasks, the scenario of unknown radio emission source is investigated. The results indicate that the proposed model is robust radio map construction and accurate in predicting the location of the emission source.Comment: 11 pages, 10 figure

Convolutional Radio Modulation Recognition Networks

We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio

Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication

In mobile systems, handoff is a vital process, referring to a process of allocating an ongoing call from one BS to another BS. The handover technique is very important to maintain the Quality of service. Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible. In this paper, it is proposed that back propagation networks and radial basis functions may be used for taking handover decision in wireless communication networks. The performance of these classifiers is evaluated on the basis of neurons in hidden layer, training time and classification accuracy. The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%

Speech Quality Classifier Model based on DBN that Considers Atmospheric Phenomena

Current implementations of 5G networks consider higher frequency range of operation than previous telecommunication networks, and it is possible to offer higher data rates for different applications. On the other hand, atmospheric phenomena could have a more negative impact on the transmission quality. Thus, the study of the transmitted signal quality at high frequencies is relevant to guaranty the user ́s quality of experience. In this research, the recommendations ITU-R P.838-3 and ITU-R P.676-11 are implemented in a network scenario, which are methodologies to estimate the signal degradations originated by rainfall and atmospheric gases, respectively. Thus, speech signals are encoded by the AMR-WB codec, transmitted and the perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.863, mostly known as POLQA. The novelty of this work is to propose a non-intrusive speech quality classifier that considers atmospheric phenomena. This classifier is based on Deep Belief Networks (DBN) that uses Support Vector Machine (SVM) with radial basis function kernel (RBF-SVM) as classifier, to identify five predefined speech quality classes. Experimental Results show that the proposed speech quality classifier reached an accuracy between 92% and 95% for each quality class overcoming the results obtained by the sole non-intrusive standard described in ITU-T Recommendation P.563. Furthermore, subjective tests are carried out to validate the proposed classifier performance, and it reached an accuracy of 94.8%